About: Over the past few years, there has been substantial effort towards automated detection of fake news. Existing research has modeled the structure, style and content of news articles, as well as the demographic traits of users. However, no attention has been directed towards modeling the properties of online communities that interact with fake news. In this work, we propose a novel approach via graph-based modeling of online communities. Our method aggregates information with respect to: 1) the nature of the content disseminated, 2) content-sharing behavior of users, and 3) the social network of those users. We empirically demonstrate that this yields significant improvements over existing text and user-based techniques for fake news detection.   Goto Sponge  NotDistinct  Permalink

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  • Over the past few years, there has been substantial effort towards automated detection of fake news. Existing research has modeled the structure, style and content of news articles, as well as the demographic traits of users. However, no attention has been directed towards modeling the properties of online communities that interact with fake news. In this work, we propose a novel approach via graph-based modeling of online communities. Our method aggregates information with respect to: 1) the nature of the content disseminated, 2) content-sharing behavior of users, and 3) the social network of those users. We empirically demonstrate that this yields significant improvements over existing text and user-based techniques for fake news detection.
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  • New York (state) lawyers
  • 2013 mergers and acquisitions
  • Pulitzer Prize for National Reporting winners
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